From 3fe08c944e7ad8998e0211d79a8eefb3f3fb2b48 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Shan=C3=A9=20Winner?=
 <43390034+swinner95@users.noreply.github.com>
Date: Sun, 28 Jul 2019 00:24:21 -0700
Subject: [PATCH] Delete label-encoder.ipynb

---
 .../how-to-guides/label-encoder.ipynb         | 169 ------------------
 1 file changed, 169 deletions(-)
 delete mode 100644 how-to-use-azureml/work-with-data/dataprep/how-to-guides/label-encoder.ipynb

diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/label-encoder.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/label-encoder.ipynb
deleted file mode 100644
index 8b4a1c69..00000000
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/label-encoder.ipynb
+++ /dev/null
@@ -1,169 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/label-encoder.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Label Encoder\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License.<br>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep has the ability to encode labels with values between 0 and (number of classes - 1) using `label_encode`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "from datetime import datetime\n",
-        "dflow = dprep.read_csv(path='../data/crime-spring.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To use `label_encode` from a Dataflow, simply specify the source column and the new column name. `label_encode` will figure out all the distinct values or classes in the source column, and it will return a new Dataflow with a new column containing the labels."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow = dflow.label_encode(source_column='Primary Type', new_column_name='Primary Type Label')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To have more control over the encoded labels, create a builder with `dataflow.builders.label_encode`.\n",
-        "The builder allows you to preview and modify the encoded labels before generating a new Dataflow with the results. \n",
-        "To get started, create a builder object with `dataflow.builders.label_encode` specifying the source column and the new column name.  "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder = dflow.builders.label_encode(source_column='Location Description', new_column_name='Location Description Label')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To generate the encoded labels, call the `learn` method on the builder object:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.learn()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To check the result, access the generated labels through the property `encoded_labels`:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.encoded_labels"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To modify the generated results, just assign a new value to `encoded_labels`. The following example adds a missing label not found in the sample data. `builder.encoded_labels` is saved into a variable `encoded_labels`, modified, and assigned back to `builder.encoded_labels`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "encoded_labels = builder.encoded_labels\n",
-        "encoded_labels['TOWNHOUSE'] = 6\n",
-        "\n",
-        "builder.encoded_labels = encoded_labels\n",
-        "builder.encoded_labels"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once the desired results are achieved, call `builder.to_dataflow` to get the new Dataflow with the encoded labels."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dataflow = builder.to_dataflow()\n",
-        "dataflow.head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file